Alation

AUGUST 4, 2022

It’s been one decade since the “ Big Data Era ” began (and to much acclaim!). Analysts asked, What if we could manage massive volumes and varieties of data? Yet the question remains: How much value have organizations derived from big data? Big Data as an Enabler of Digital Transformation.

Big Data 95

Big Data Big Data Apache Kafka Data Lakes 95

Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. These systems include data pipelines, data warehouses, and data lakes, among others.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. It is often used as a foundation for enterprise data lakes.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

Data Lakes 59

Data Lakes Data Warehouse Data Quality Data Governance 59

Pickl AI

DECEMBER 2, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

Such growth makes it difficult for many enterprises to leverage big data; they end up spending valuable time and resources just trying to manage data and less time analyzing it. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Dataconomy

SEPTEMBER 4, 2023

Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data. Big data analytics is essential for organizations dealing with large-scale data, such as social media platforms, e-commerce giants, and scientific research.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. The data lake environment is required to configure an AWS Glue database table, which is used to publish an asset in the Amazon DataZone catalog.

Machine Learning 126

Machine Learning Machine Learning Data Governance ML 126

AWS Machine Learning Blog

FEBRUARY 21, 2025

He specializes in large language models, cloud infrastructure, and scalable data systems, focusing on building intelligent solutions that enhance automation and data accessibility across Amazons operations. Rajesh Nedunuri is a Senior Data Engineer within the Amazon Worldwide Returns and ReCommerce Data Services team.

AWS 129

AWS Natural Language Processing Machine Learning Machine Learning 129

The MLOps Blog

JUNE 27, 2023

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

AWS Machine Learning Blog

AUGUST 15, 2024

When SageMaker Data Wrangler finishes importing, you can start transforming the dataset. After you import the dataset, you can first look at the Data Quality Insights Report to see recommendations from SageMaker Canvas on how to improve the data quality and therefore improve the model’s performance.

ML 129

ML ML Data Preparation AWS 129

Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Precisely

OCTOBER 16, 2023

Big data analytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage. Organizations have come to understand that they can use both internal and external data to drive tremendous business value. Secure data exchange takes on much greater importance.

Analytics 72

Analytics Analytics Big Data Analytics Big Data Analytics 72

Alation

FEBRUARY 13, 2020

This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata. Data catalogs have become the standard for metadata management in the age of big data and self-service analytics.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

Pickl AI

OCTOBER 6, 2024

This phase is crucial for enhancing data quality and preparing it for analysis. Transformation involves various activities that help convert raw data into a format suitable for reporting and analytics. Normalisation: Standardising data formats and structures, ensuring consistency across various data sources.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Dataversity

JANUARY 6, 2021

We live in an era of big data. Amazingly, statistics show that around 90 percent of this data is only two years old. However, Data Management and structuring are notoriously complex. […]. The post The Need for Flexible Data Management: Why Is Data Flexibility So Important?

Big Data 64

Big Data Big Data Data Lakes Data Governance 64

Flipboard

MAY 9, 2025

Additional considerations Though the potential of this approach is significant, there are several challenges to consider: Data quality High-quality, diverse input data is key to effective model performance. He is focused on big data, data lakes, streaming and batch analytics services, and generative AI technologies.

AWS 100

AWS Natural Language Processing AI AI 100

Pickl AI

AUGUST 1, 2023

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Alation

MARCH 21, 2023

To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations. What is big data in the travel and tourism industry? Using Alation, ARC automated the data curation and cataloging process. “So

Analytics 52

Analytics Analytics Data Silos Big Data 52

DagsHub

NOVEMBER 11, 2024

In general, this data has no clear structure because it may manifest real-world complexity, such as the subtlety of language or the details in a picture. Advanced methods are needed to process unstructured data, but its unstructured nature comes from how easily it is made and shared in today's digital world.

AI 52

AI AI Data Lakes Database 52

IBM Journey to AI blog

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way.

AI 93

AI AI Data Scientist Data Governance 93

Precisely

JANUARY 19, 2023

Organizations that can master the challenges of data integration, data quality, and context will be well positioned to identify opportunities and threats quickly, and then to take decisive action to gain competitive advantage. Today, companies realize the true potential of that capability.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

ODSC - Open Data Science

OCTOBER 20, 2023

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform.

AI 52

AI AI Data Science Machine Learning 52

Pickl AI

JUNE 11, 2024

In-Memory Computing This technology allows for storing and processing data in RAM for faster query response times, enabling real-time analytics. Big Data Integration Data warehouses are increasingly incorporating big data technologies to handle vast volumes of data from diverse sources.

Data Warehouse 52

Data Warehouse ETL Data Mining Data Mining 52

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. This ensures that the data is accurate, consistent, and reliable.

Data Pipeline 59

Data Pipeline ETL Data Quality SQL 59

AWS Machine Learning Blog

JANUARY 26, 2024

Also consider using Amazon Security Lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in your account. Ram Vittal is a Principal ML Solutions Architect at AWS.

AWS 140

AWS ML ML AI 140

AWS Machine Learning Blog

NOVEMBER 12, 2024

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

AWS 126

AWS AI AI Data Lakes 126